Simpler Than You Think: The Practical Dynamics of Ranked Choice Voting

TL;DR

This study empirically demonstrates that Ranked Choice Voting (RCV), despite theoretical criticisms, is practically simple, transparent, and enhances electoral competitiveness, with minimal manipulation and ballot exhaustion effects across diverse U.S. elections.

Contribution

The paper provides the first large-scale empirical analysis showing RCV's practical simplicity, robustness, and democratic benefits, supported by a novel computational approach for election analysis.

Findings

RCV closely mirrors plurality election transparency in practice.

Post-adoption, competitiveness increased with smaller margins of victory.

Ballot exhaustion minimally impacts election outcomes.

Abstract

Ranked Choice Voting (RCV) adoption is expanding across U.S. elections, but faces persistent criticism for complexity, strategic manipulation, and ballot exhaustion. We empirically test these concerns on real election data, across three diverse contexts: New York City's 2021 Democratic primaries (54 races), Alaska's 2024 primary-infused statewide elections (52 races), and Portland's 2024 multi-winner City Council elections (4 races). Our algorithmic approach circumvents computational complexity barriers by reducing election instance sizes (via candidate elimination). Our findings reveal that despite its intricate multi-round process and theoretical vulnerabilities, RCV consistently exhibits simple and transparent dynamics in practice, closely mirroring the interpretability of plurality elections. Following RCV adoption, competitiveness increased substantially compared to prior plurality elections, with average margins of victory declining by 9.2 percentage points in NYC and 11.4 points in Alaska. Empirically, complex ballot-addition strategies are not more efficient than simple ones, and ballot exhaustion has minimal impact, altering outcomes in only 3 of 110 elections. These findings demonstrate that RCV delivers measurable democratic benefits while proving robust to ballot-addition manipulation, resilient to ballot exhaustion effects, and maintaining transparent competitive dynamics in practice. The computational framework offers election administrators and researchers tools for immediate election-night analysis and facilitating clearer discourse around election dynamics.